# R/ta1.2.R In jlthomps/EflowStats: Returns HIT/HAT Hydrologic Indicator stats for a given set of data

#### Documented in ta1.2

```#' Function to return the TA1 and TA2 hydrologic indicator statistics for a given data frame
#'
#' This function accepts a data frame that contains a column named "discharge" and calculates
#' TA1; Constancy. Constancy is computed via the formulation of Colwell (see example in Colwell, 1974). A matrix of values
#' is compiled where the rows are 365 (no February 29th) days of the year and the columns are 11 flow categories. The
#' cell values are the number of times that a flow falls into a category on each day. The categories are:
#' log(flow) < 0.1 ? log(mean flow),
#' 0.1 ? log(mean flow) ??? log(flow) < 0.25 ? log(mean flow)
#' 0.25 ? log(mean flow) ??? log(flow) < 0.5 ? log(mean flow)
#' 0.5 ? log(mean flow) ??? log(flow) < 0.75 ? log(mean flow)
#' 0.75 ? log(mean flow) ??? log(flow) < 1.0 ? log(mean flow)
#' 1.0 ? log(mean flow) ??? log(flow) < 1.25 ? log(mean flow)
#' 1.25 ? log(mean flow) ???log(flow) < 1.5 ? log(mean flow)
#' 1.5 ? log(mean flow) ??? log(flow) < 1.75 ? log(mean flow)
#' 1.75 ? log(mean flow) ??? log(flow) < 2.0 ? log(mean flow)
#' 2.0 ?log(mean flow) ??? log(flow) < 2.25 ? log(mean flow)
#' log(flow) ??? 2.25 ? log(mean flow)
#' The row totals, column totals, and grand total are computed. Using the equations for Shannon information theory
#' parameters, constancy is computed as:
#' 1- (uncertainty with respect to state)/log (number of state)
#' TA2; Predictability. Predictability is computed from the same matrix as constancy (see example in Colwell, 1974). It
#' is computed as:
#' 1- (uncertainty with respect to interaction of time and state - uncertainty with respect to time)/log (number of state)
#' where uncertainty with respect to state = sum((YI_sub/Z)*log10(YI_sub/Z))
#' where YI_sub = the non-zero sums of the 11 categories and Z = the sum total of the Colwell matrix
#' and where uncertainty with respect to time = sum((XJ_sub/Z)*log10(XJ_sub/Z))
#' where XJ_sub = the non-zero sums of the 365 days
#' and where uncertainty with respect to interaction of time and state = sum((colwell_sub/z)*log10(colwell_sub/Z))
#' where colwell_sub = the non-zero sums of the entire matrix
#' and where number of state = number of categories = 11
#'
#' @param qfiletempf data frame containing a "discharge" column containing daily flow values
#' @return ta1.2 list containing TA1 and TA2 for the given data frame
#' @export
#' @examples
#' qfiletempf<-sampleData
#' ta1.2(qfiletempf)
ta1.2 <- function(qfiletempf) {
colwell_mat <- matrix(-99999,365,11)
mean_flow <- ma1(qfiletempf)
for (i in 1:365) {
m <- ifelse(i<93,i+273,i-92)
qfile_sub <- qfiletempf[qfiletempf\$jul_val==m,]
colwell_mat[i,1] <- nrow(qfile_sub[log10(qfile_sub\$discharge)<(.1*log10(mean_flow)),])
colwell_mat[i,2] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(.1*log10(mean_flow)) & log10(qfile_sub\$discharge)<(.25*log10(mean_flow)),])
colwell_mat[i,3] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(.25*log10(mean_flow)) & log10(qfile_sub\$discharge)<(.5*log10(mean_flow)),])
colwell_mat[i,4] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(.5*log10(mean_flow)) & log10(qfile_sub\$discharge)<(.75*log10(mean_flow)),])
colwell_mat[i,5] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(.75*log10(mean_flow)) & log10(qfile_sub\$discharge)<(1*log10(mean_flow)),])
colwell_mat[i,6] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(1*log10(mean_flow)) & log10(qfile_sub\$discharge)<(1.25*log10(mean_flow)),])
colwell_mat[i,7] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(1.25*log10(mean_flow)) & log10(qfile_sub\$discharge)<(1.5*log10(mean_flow)),])
colwell_mat[i,8] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(1.5*log10(mean_flow)) & log10(qfile_sub\$discharge)<(1.75*log10(mean_flow)),])
colwell_mat[i,9] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(1.75*log10(mean_flow)) & log10(qfile_sub\$discharge)<(2*log10(mean_flow)),])
colwell_mat[i,10] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(2*log10(mean_flow)) & log10(qfile_sub\$discharge)<(2.25*log10(mean_flow)),])
colwell_mat[i,11] <- nrow(qfile_sub[log10(qfile_sub\$discharge)>=(2.25*log10(mean_flow)),])
}
XJ <- rowSums(colwell_mat)
YI <- colSums(colwell_mat)
Z <- sum(colwell_mat)
XJ_sub <- XJ[XJ>0]
HX <- -sum((XJ_sub/Z)*log10(XJ_sub/Z))
YI_sub <- YI[YI>0]
HY <- -sum((YI_sub/Z)*log10(YI_sub/Z))
colwell_sub <- colwell_mat[colwell_mat>0]
HXY <- -sum((colwell_sub/Z)*log10(colwell_sub/Z))
HxY <- HXY - HX
ta1 <- 1-(HY/log10(11))
ta2 <- 100*(1-(HxY/log10(11)))
ta1.2<-list(ta1=ta1,ta2=ta2)
return(ta1.2)
}
```
jlthomps/EflowStats documentation built on May 19, 2019, 12:48 p.m.